CROSS-REFERENCE TO RELATED APPLICATION
This application claims the benefit of U.S. Provisional Patent Application 61/495,584 filed Jun. 10, 2011 entitled “Prospect Assessment and Play Chance Mapping Tools,” the entirety of which is incorporated by reference herein.
A prospect includes an area of exploration in which hydrocarbons have been predicted to exist in economic quantity. A prospect may include an anomaly, such as a geologic structure or a seismic amplitude anomaly that is recommended by explorationists for drilling a well. Justification for drilling a prospect is made by assembling evidence for an active petroleum system, or reasonable probability of encountering reservoir-quality rock, a trap of sufficient size, adequate sealing rock, and appropriate conditions for the generation and migration of hydrocarbons to fill the trap. A single drilling location is also called a prospect, but the term is generally used in the context of exploration: exploration prospect assessment (EPA), hereinafter referred to as Prospect Assessment (PA).
A group of prospects of a similar nature constitutes a play. Thus, a play is a region in which hydrocarbon accumulations or prospects of a given type may occur: a conceptual model for a style of hydrocarbon accumulation used by explorationists to develop prospects in a basin, region, or trend and used by development personnel to continue exploiting a given trend. A play (or a group of interrelated plays) may occur in a single petroleum system.
Common Risk Segment Mapping (CRSM) is an exploration method to define areas of low exploration risk. Certain companies employ some method of play fairway mapping and common risk mapping. These may be used to define play Chance of Success (play COS) at the play level and local prospect Chance of Success (prospect COS) at the prospect level. “Traffic light” maps of red, yellow and green for high, moderate and low risk areas are examples of displays in the industry. CRSM maps that combine the geological elements that determine the Chance of Success of plays and prospects may be further combined with maps that delineate other risk elements that affect the overall prospectivity in an area, for example, distance from shore, water depth, accessibility to acreage, and so forth.
Play-based exploration may have a different focus than prospect-based exploration. Beyond the traffic light maps, there may be maps that show shared/play-specific and local/prospect-specific probabilities. A problem with these conventional probability and Chance of Success maps, however, may be the relative complexity of arriving at the map itself, such that if a geological condition changes, or when the explorationist changes a hypothetical or a geological property underpinning the map, the map has to be reconfigured and recalculated, which may be a conventionally painstaking process.
Play fairway mapping, common risk mapping, and Chance of Success mapping conventionally depend on numerous complex processes. The shear amount of input data through which the user may need to sort can make map creation difficult and sometimes non-intuitive. Additionally, there may be a lack of information on how to accomplish the exploration workflows. Easy-to-use tools may be needed to give fast results and simplify the clutter of inputting data for the process of creating the Chance of Success maps and evaluating the results.
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Prospect assessment and play chance mapping tools are provided. For exploration prospect assessment of potential hydrocarbon resources in a play or a prospect, an example system provides dynamically linked, real time risk, chance of success, and chance of failure maps (“chance maps”), transformed in real time from the geological properties of one or more input geological maps, play fairway maps, or other input data. The geological maps and data input to the system are dynamically linked to the resulting output: chance maps, so that a change to a geologic parameter of an input map or input datum automatically updates the chance map(s) in real time or near real time. In an example implementation, user-instigated changes in an example user interface are also instantly reflected in the resulting chance map. The example user interface allows the user to create and specify a custom hierarchical matrix of risk maps, including specifying dynamically linked input maps and data, and the dynamic links themselves. The user can specify sub-maps and sub-matrices to construct the main risk matrix, selecting and dropping maps directly into the matrix. A customizable transform quickly converts geologic properties from the geologic domain to the chance domain. The user interface also enables the user to navigate geological maps, draw a polygon around areas of interest (AOI) or otherwise select areas on a geologic map. After selecting an area, the user may drag-and-drop geologic properties within the polygon directly into an uncertainty engine that maps risk by applying an equation or by building a distribution to map uncertainty in a manner that is automatically tied directly back to geologic reality. A merge tool can apply a customizable formula to perform a programmatic merge of multiple grids that are modeling multiple different geological interpretations of a prospect. The merge tool outputs a single chance of success value for multiple geologic property values at each grid node.
This summary section is not intended to give a full description of prospect assessment and play chance mapping tools, or to provide a comprehensive list of features and elements. A detailed description with example implementations follows.
BRIEF DESCRIPTION OF THE DRAWINGS
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FIG. 1 is a block diagram of an example system and environment for prospect assessment and play chance mapping tools.
FIG. 2 is a diagram of an example play chance matrix.
FIG. 3 is a diagram of an example transform table.
FIG. 4 is a diagram of an example property to chance of success map conversion via transform.
FIG. 5 is a diagram of an example process of selecting an area of a geological map to drag-and-drop property values into a distribution for creating a live chance of success map.
FIG. 6 is a flow diagram of an example process setting up a chance of failure map.
FIG. 7 is a diagram of an example histogram or distribution builder for creating a chance of failure map.
FIG. 8 is a diagram of an example merge process for generating a single chance of success value for a distribution of geologic values at each grid node of a grid that is modeling a play or prospect.
FIG. 9 is a flow diagram of an example process for inputting maps to generate a risk map.
FIG. 10 is a flow diagram of the example process in FIG. 9 with an uncertainty option.
FIG. 11 is a flow diagram of the example process in FIG. 10, with an auto update option.
FIG. 12 is a diagram of an example user interface for creating a chance map.
FIG. 13 is a diagram of an example user interface showing default templates.
FIG. 14 is a diagram of an example user interface showing icons or buttons for creating and linking input maps and risk maps.
FIG. 15 is a diagram of an example user interface showing creation of submaps during matrix and map creation.
FIG. 16 is a diagram of an example user interface showing matrix handling.
FIG. 17 is a diagram of an example user interface showing matrix creation.
FIG. 18 is a diagram of an example user interface showing value entering during matrix creation.
FIG. 19 is a diagram of an example user interface showing an option for loading a pre-made matrix.
FIG. 20 is a diagram of an example user interface showing how to input a play-fairway map 122.
FIG. 21 is a diagram of an example user interface showing input of a single value via typing or scaling on a visual slider.
FIG. 22 is a diagram of an example user interface showing how to link a pre-existing risk map and/or a play-fairway map 122.
FIG. 23 is a diagram of an example user interface showing how to create a link between an input map and a desired risk map.
FIG. 24 is a diagram of an example user interface showing how to specify a transform through a table format.
FIG. 25 is a diagram of an example user interface showing entry of matrix values.
FIG. 26 is a diagram of an example user interface showing entry of matrix values.
FIG. 27 is a diagram of an example user interface showing a linkage indicator to show when maps are dynamically linked.
FIG. 28 is a diagram of an example user interface showing a control for activating automatic updating between maps.
FIG. 29 is a diagram of an example user interface showing an alternate method of linking maps for real time updating.
FIG. 30 is a diagram of an example user interface showing output options.
FIG. 31 is a diagram of an example user interface showing selection of uncertainty options for a single map.
FIG. 32 is a diagram of an example user interface showing selection of uncertainty options for multiple maps.
FIG. 33 is a diagram of an example user interface showing a map stack option, in which a user can enter a stack of maps within a folder and select a weighting factor which to skew the distribution.
FIG. 34 is a diagram of an example user interface showing a test button to check if there are missing data maps or value entries and if there are current connections between the data maps and the risk maps.
FIG. 35 is a diagram of an example user interface showing an example test result of the test in FIG. 34.
FIG. 36 is a flow diagram of an example method of creating a live chance of success map.
FIG. 37 is a flow diagram of an example method of capturing geological properties to generate a live chance of success map.
FIG. 38 is a flow diagram of an example method of merging multiple geological grids into a single grid of chance of success values.
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This disclosure describes prospect assessment and play chance mapping tools. An example system streamlines information handling and provides a friendly and comprehensive user interface to construct custom risk matrices and dynamically link geological property maps and other input data to resulting chance maps and uncertainty assessments. The terms “chance” and “risk” are used somewhat interchangeable herein. Resulting chance (risk) maps may be live with real time automatic updating when there is a change, for example, when there is a change in a dynamically linked geological property map or a user-initiated change in a hypothetical parameter.
Example systems may thus provide dynamically linked chance maps, transformed in real time from geological properties and other input data. Users can generate a custom risk matrix dynamically linking geological maps with chance maps via comprehensive interface tools, for example, by dragging-and-dropping maps directly into the matrix. A customizable transform may programmatically convert the geologic domain to the chance domain. The user can navigate input maps, select areas of interest, and drag-and-drop geologic properties directly into an uncertainty engine and distribution builder for uncertainty assessment based directly on geologic reality. A merge tool can programmatically unify multiple geological interpretations (multiple maps) of the same prospect. The merge tool may output a single chance of success value at each grid node for multiple geologic property values at each corresponding grid node across the multiple grid maps.
FIG. 1 shows an example system, providing an environment for prospect assessment and play chance mapping tools, such as mapping tools 100. A computing device 102 may implement components, such as simulators 104 and an example, representative set of the mapping tools 100. The simulators 104 may include seismic-to-simulation programs and software suites, geological simulators, reservoir simulators, oilfield modelers, and so forth. The example mapping tools 100 may include an example data capture tool 106, mapping engine 108, transforms 110, matrix builder 112, user interface manager 114, merge tool 116, distribution builder 118, uncertainty engine 120, and other modules: for exploration and geological prospecting, risk mapping, chance of success (or failure) studies and mapping, resource and site assessment, etc. The mapping tools 100 are illustrated as software, but can be implemented as hardware or as a combination of hardware, and software instructions. The illustrated set of mapping tools 100 is provided as an example for the sake of description, other mapping tools, or other configurations of the mapping tools 100 can also be used.
In the illustrated example, the computing device 102 receives geologic maps 122 and other data as input. One or more of the geologic maps 122 may show at least one geological property 124 and may be communicatively coupled via sensory and control devices with real-world subsurface earth volumes 126, i.e., underground plays including petroleum reservoirs, depositional basins, seabeds, oilfields, wells, etc., as well as surface control networks, and so forth. A subsurface earth volume 126 being modeled may be a candidate for petroleum production, or for water resource management, carbon services, or other uses.
The computing device 102 hosting the mapping tools 100 may be a computer, computer network, or other device that has a processor 128, memory 130, data storage 132, and other associated hardware such as a network interface 134 and a media drive 136 for reading and writing a removable storage medium 138. The removable storage medium 138 can be, for example, a compact disk (CD); digital versatile disk/digital video disk (DVD); flash drive, etc.
The removable storage medium 138 may include instructions for implementing and executing the example mapping tools 100 and associated computer-executable methods (e.g., see FIGS. 36-38 and associated descriptions). At least some parts of the mapping tools 100 may be stored as instructions on a given instance of the removable storage medium 138, removable device, or in local data storage 132, to be loaded into memory 130 for execution by the processor 128. Although the illustrated mapping tools 100 are depicted as programs residing in memory 130, they may also be implemented as hardware, such as application specific integrated circuits (ASICs) or as a combination of hardware and software.
In an example implementation of this example system, the computing device 102 may receive field data via the network interface 134, in the form of maps 122, derived from seismic data 140 and well logs 142 from geophones, well measurement devices, and other sensors at a potential petroleum field or other subsurface earth volume 126.
A user interface manager 114 and display controller 144 may extend an associated user interface 146 on a display 150 (and input/output for mouse, pointing devices, keyboard, touch screen, etc.), as well as geologic model images 148, such as a 2D or 3D visual representation of layers or rock properties in a subsurface earth volume 126. The displayed geologic model images 148 may generated by the mapping tools 100. The mapping tools 100 may perform other modeling operations and generate useful user interfaces 146 via the display controller 144, including novel interactive graphics, for user control of processes generating Chance of Success maps 152 or other maps.
In an example implementation, the chance maps 152, representatively called Chance of Success maps 152 herein (also known as and alternatively cast as risk maps or chance of failure maps), can also be utilized to generate control signals to be used via control devices in real world prospecting, modeling, exploration, prediction, and/or control of resources, such as petroleum production, water resource management, carbon services, etc., including direct control via hardware control devices of such resources as drilling, injection and production wells, reservoirs, fields, transport and delivery systems, and so forth.
Example General Operation
In an example implementation, the example system can generate a living play chance map 152 from geological properties 124 or attributes inherent in the input geologic maps 122 (for example, porosity). When there is a change in the geological properties 124, the generated play chance map 152 may adapt in real time to provide updated risk or Chance of Success features, maps 152, and output. Thus, an example system provides a dynamic play chance map 152 that can show, for example, Chance of Success in real time, based on changing geological properties 124 or user-initiated hypotheticals, e.g., as entered via the example user interface 146.
The example user interface 146 can access the matrix builder 112 for creating Chance of Success maps 152 (e.g., prospect assessment) and enables the user to create and specify a custom hierarchical matrix of risk maps, including the dynamically linked input maps and data, and the dynamic links themselves. The user can specify sub-maps and sub-matrices for construction of the main risk matrix, and can select and drop maps and other matrices directly into the main matrix.
When provided with a geologic property map 122, or with selected representative geologic property values 124 from maps 122, the system may apply one or more customizable transforms 110 to programmatically generate the chance map 152, which in turn may then be compiled into or used as a precursor for a larger, overall chance map 152, e.g., for common risk segment mapping (CRSM).
For uncertainty assessment, the uncertainty engine 120 may provide visual and navigation tools via the user interface 146 for enabling the user to harvest a geological property 124 of interest directly from the geologic maps 122. The desired parameter values 124 can also be entered manually, in a direct manner. The user can draw a polygon around an area of interest (AOI) on a geologic map 122 to collect parameter values of the property 124 and then “drag-and-drop” the selected visual region containing the desired property values 124 directly into an uncertainty mapping capability of the uncertainty engine 120 or distribution builder 118, which may apply a Monte Carlo simulation. Specifically, the user interface manager 114 may enable the user to obtain minimum, peak, and maximum petroleum-system parameter values from a map 122 with user-friendly visual selection tools, which then feed the distribution builder 118 to perform uncertainty and prospect assessment. By obtaining geological data directly from the geological map(s) 122, values in the distribution and thus the uncertainty assessment are tied directly to geologic reality without conventional guesswork.
In an example implementation, an example system may build a distribution for each grid node in multiple 2D or 3D models of a resource. In a grid-node-to-grid-node manner, an example technique and merge tool 116 converts multiple petroleum-system parameter coefficients that result from multiple geologic interpretations, into a single Chance of Success value for each grid-node. The merge tool 116 develops an integrated Chance of Success map 152, combining multiple geologic scenarios (multiple maps of the same prospect) into a single summary expression of Chance of Success for a parameter at each grid node of a single resulting map 152.
When provided with a geologic property map 122, or with selected representative geologic property values 124 from maps 122, an example mapping tool 100 applies one or more transforms 110 to programmatically generate the chance map 152, which in turn may then be compiled into, or used as, a precursor for an overall chance map 152, for example, a common risk segment map 152.
A property-to-chance transform 110 for play chance mapping can be viewed as a function converting a geologic property at each grid-node in a model of a surface or in a model of a subsurface volume 126 into a chance of success value. Thus, a chance of success value at each grid-node may be determined from a geologic property through the property-to-chance transform 110. Chance of success (COS) is used representatively herein, but chance of failure can also be used, where COF=1−COS.
In order to estimate the chance of success for a given play to be feasible, the matrix builder 112 may decompose the change into sub-elements (COS for a reservoir, for a seal, for a trap, etc.). Each of these sub-elements can be split up still further into lower levels. For example, COS for a reservoir may include a combination of COS for reservoir presence and COS for reservoir quality, thereby building a matrix that has a desired degree of complexity.
FIG. 2 shows an example (e.g., simplified) play chance matrix 200 (i.e., risk matrix 200). The matrix 200 defines the nature and characteristics of the final COS map 152. In order to populate the sub-element chance maps 152 at the lowest level elements that branch (toward the right side 202 of the matrix) the matrix builder 112 may utilize some geological arguments. As an example, porosity can be used as a geologic property 124 in order to define the reservoir quality (another geologic property could just as easily be used as a representative example). A geologist making an evaluation via the example mapping tool 100 can qualify the porosity as “good” and therefore decide for a high COS (or low COF) for reservoir quality and use this value in matrix construction for further calculations of play chance.
In some simulators 104, (e.g., PETREL, which is developed and distributed by Schlumberger, Ltd, Houston Tex. and its affiliates) the geologist can easily quantify porosity with a porosity map 122 for the given reservoir. The porosity map 122 may have a certain range of porosity values, varying with X and Y position, e.g., from approximately 5% to approximately 20%. The geologist may estimate, for example, that below a porosity of approximately 8%, the reservoir quality may considered “bad”, and a porosity of more than approximately 15% may be considered “good.”
The geologist may also define “good” and “bad” via the matrix builder 112. As an example, with perfect data quantity and quality and a reasonably correct geological interpretation, “good” can mean COS=1 (COF=0) and “bad” can mean COS=0 (COF=1). However, in certain cases, e.g., in frontier exploration, data and interpretation may be highly uncertain, so a geologist\'s definition of “good” might not exclude failure and “bad” might not exclude success. As an example, this can mean that “good” may have a COS<1 (COF>0) and “bad” a COS>0 (COF<1). In an example, COS<=0.7 (COF>=0.3) may be used for “good” and COS=0.3 (COF=0.7) may be used for “bad” (the foregoing are merely example values, and other ranges are possible). This limitation of the COS (COF) scale may prevent the geologist from terminating prospect exploration of an area with an unduly “bad” result or giving an unduly high recommendation to another area with a “good” result. The resolution of the uncertainty issue may be useful to the interpretation.
In an example implementation, both the geological arguments (e.g., porosity 302) and the chance (COS/COF) 304 may constitute a transform 110 and may be entered into a table 300 by the geologist, such as, for example, the property-to-chance transform table 300 for porosity shown in FIG. 3.
At each cell of an input porosity map 122, the porosity value 124 may be transformed into chance of success (COS value) using the property-to-chance transform 110. As an example, the minimum porosity value of the map 122 (approximately 5%) may be assigned a COS of 0.3, which may be the same as a porosity of approximately 8% (“bad”). A porosity of approximately 10% (between “good” and “bad”) may be assigned a COS of 0.5, and porosities>=approximately 12% (“good”) may be assigned a COS of 0.7.
FIG. 4 shows how a continuous porosity map 122 may be transformed into a continuous chance of success (COS) map 152 (limited to values between 0.3 and 0.7) using the example property (e.g., porosity-to-chance (COS) transform 110.
In an example implementation, the mapping engine 108 applies the transform 110 to execute real time updating of the COS map 152.
Example Capture of Matrix and Map Data
An example data capture tool 106 implemented by the user interface manager 114 may be used to gather geological data, such as geologic maps 122 (e.g., play fairway maps) for construction of the risk matrix 200 and geological property data 124 from the geologic maps 122 for uncertainty studies and also for matrix construction. In an example embodiment, the data capture tool 106 gathers real property values from a geologic map 122 and ties an expression of uncertainty in chance of success mapping back to geological reality—i.e., instead of basing the uncertainty on guesswork or reliance on pure intuition as in conventional techniques.
In a prospect assessment (PA) setting, the example data capture tool 106 may target a workflow in exploration—e.g., that of prospect assessment and ranking utilizing the Monte Carlo process. The result may include an estimate of a range of in-place and recoverable hydrocarbon resources (e.g., oil, free gas, solution gas, condensate, etc.).
Early in a prospect assessment process, it may be useful to determine whether or not a particular prospect is a practical investment opportunity. At the early assessment stages, little information may be available and there may exist uncertainty regarding petroleum-system parameters (charge, timing, migration, reservoir, trap, seal, recovery, etc.).
A stochastic process may allow an explorationist to express, without having to provide statistical input (variance, kurtosis, mean, standard deviation, and so forth) the uncertainty regarding primary petroleum-system variables. Stochastic processing may result in a range of possible recoverable resources, an estimate of chance of technical success, an estimate of chance of economic success, and separate lists of parameters that may contribute to potential failure and to uncertainty in the volume of recoverable hydrocarbons.
In an example implementation, the uncertainty engine 120 may capture or assess uncertainty using a distribution of values 504 as illustrated, for example, in FIG. 5. In an example distribution-building scheme, such distributions may be built, in part, by the user supplying some combination of minimum, peak, and maximum values.
Human explorationists may supply coefficients for minimum, peak, and maximum values by applying their geologic intuition regarding analogous situations they have experienced or by making estimates of such coefficients by inspection of maps 122. The example data capture tool 106 described herein may facilitate the automatic calculation of minimum, peak, and maximum values for a petroleum-system parameter, and seamless passage of these coefficients to the uncertainty engine 120 and prospect assessment distribution builder 118 so that uncertainty about that parameter can be utilized in Monte Carlo resource volume calculations. In an example implementation, the uncertainty engine 120 and distribution builder 118 may automatically derive minimum, peak, and maximum values representing three geologic scenarios that describe the range of possibilities for a property parameter value 124 in a particular play.
A feature of prospect assessment is its ability to utilize diverse input: data derived from various maps 122 and map polygons generated in play chance mapping, common risk segment mapping (CRSM), and other functions of simulators 104, such as PETREL.
The subsurface petroleum-system parameter “porosity” will again be used to illustrate aspects of the example data capture tool 106. The process described below, however, can apply to any petroleum-system parameter 124 about which the explorationist is uncertain (for which a data distribution would be uncertain) and which contributes to the calculation of hydrocarbon resources.
In an example implementation, an example workflow mediated and facilitated by the mapping tools 100 proceeds with the explorationist creating, for example, three maps 122 representing three different geologic scenarios. These scenarios, for example, may include one or more of the following: